Object segmentation enables identification of objects in a digital video stream or image and can be used to simplify and/or change the representation of an image into something that is more meaningful and/or is easier to analyze by computer. For example, object segmentation is key to many robotic vision applications. Most vision-based autonomous vehicles acquire information regarding the location of obstacles in their surroundings by identifying the obstacles in a stream of video. It addition, object segmentation can be used for high-level image understanding and scene interpretation such as spotting and tracking events in surveillance video. In particular, pedestrian and highway traffic can be regularized using density evaluations obtained by segmenting people and vehicles. Using object segmentation, speeding and suspicious moving cars, road obstacles, and other uncommon activities may be detected.
However, typical object segmentation algorithms are configured to perform object segmentation of image data in two or even three dimensions in real-time. Thus, because of the complex nature of most object segmentation algorithms, and because of the large quantities of video information processed in real-time, most object segmentation algorithms are computationally intensive and have to be executed on high-end computational devices.
Designers and users of object segmentation methods and systems continue to seek enhancements of object segmentation in digital images.